Untitled
Shanie Lynn Jorgenson
2025-11-26
Introduction
Bees offer important services that directly influence Oregon’s mosaic of ecosystems. Bees help to shape habitat structure by supporting plant reproduction, this in turn contributes to the resilience of the ecosystem. Bee communities respond strongly to spatial heterogeneity in different landscapes and recent work sheds light on bee assemblages not being uniform across regions. Species instead differ in abundance, habitat affinity and distribution as environmental conditions change across the landscape (Christman et al., 2024).
There are studies that have been done in the Pacific Northwest that give insight into how geographic gradients and habitat structure can aid in shaping bee populations. Patterns of species diversity in bumble bee communities among mountain ranges suggest climate and vegetation type shape bee dispersal and community turnover (Koch et al. 2017). These finding tell us that habitat conditions and land-use patterns collectively shape the structure of bee communities. Forest structure, succesional stages and disturbance history can support very different bee assemblages. Bee diversity could decline dramatically following a timber harvest and remain low for decades (Zitomer et al., 2023). Forests of high density and closed canopies can reduce abundancy and connectivity between habitat patches (Ryleigh et al., 2023). This evidence points to sensitivity of bee communities to vegetation structure or land-cover type. Despite the strong evidence that bee communities are shaped by specific habitat features There are limited studies explicitly linking bee communities to specific land-cover types across Oregon’s ecoregions. Studies have examined regional bee diversity or landscape influences on diversity, but few have sought to quantify how species abundance and community composition vary across specific land-cover categories with in the Cascade and Coastal ranges of Oregon. Research like this matters because changing climate, land-cover and disturbance can restructure community composition and affect biodiversity and ecosystem function. Understanding how regional landscape differences influence bee diversity is essential for habitat and pollinator conservation as well as land use planning or forest management in Oregon.
Questions: How does bee abundance vary between the Coastal and Cascade regions? Is the dominant species for each region different? How do distributions vary across land-cover types for each region?
Expectations: Given this information I expect that land-cover differences influence bee abundance and species composition in each region.
library(dplyr)##
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library(raster)## Loading required package: sp
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## selectlibrary(RColorBrewer)
library(sf)## Linking to GEOS 3.13.0, GDAL 3.8.5, PROJ 9.5.1; sf_use_s2() is TRUElibrary(terra)## terra 1.8.80
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## Attaching package: 'terra'
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## extractlibrary(ggplot2)
library(vegan)## Loading required package: permutelibrary(viridis)## Loading required package: viridisLiteData and Data Wrangling
For this project I used 3 different data sets. The first was Oregon Bee Atlas (OBA), second was the USGS National Land Cover Database (NLCD), and finally Oregon ecoregion data. To be able to use this data I needed to be able to join all three togather. I first started off with reading in the .csv file then filtering through my OBA Data. I first filtered out any records with missing species or location data. Next I rastered the NLCD, viewed the crs. There were 20 land-cover types for the NLCD raster. I then organized the land cover labels to their own table for easy use when creating legends for visuals. I finally read in the ecoregion data as a shape file.
Load and clean up the OBA data
# OBA Data:
oba <- read.csv("Data-sets/OBA_2018-2024.csv", stringsAsFactors = FALSE)
oba <- oba %>%
filter(!is.na(genus), !is.na(specificEpithet), # Filter out missing name data.
genus != "", specificEpithet != "") %>%
# Combine genus and specificEpithet columns.
mutate(GenusSpecies = paste(genus, specificEpithet, sep = " ")) %>%
# Filter out the columns I want to start with.
dplyr::select(day, month, year, genus, GenusSpecies, verbatimElevation, decimalLatitude, decimalLongitude) %>%
filter(!is.na(decimalLatitude), !is.na(decimalLongitude)) %>%
mutate(verbatimElevation = as.numeric(verbatimElevation)) %>%
filter(verbatimElevation >= 0 & verbatimElevation <= 6000) ## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `verbatimElevation = as.numeric(verbatimElevation)`.
## Caused by warning:
## ! NAs introduced by coercionglimpse(oba)## Rows: 85,763
## Columns: 8
## $ day <int> 4, 4, 4, 16, 22, 22, 22, 22, 22, 2, 2, 17, 17, 17, 1…
## $ month <chr> "5", "5", "5", "5", "6", "6", "6", "6", "6", "8", "8…
## $ year <int> 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018…
## $ genus <chr> "Andrena", "Andrena", "Andrena", "Bombus", "Bombus",…
## $ GenusSpecies <chr> "Andrena prunorum", "Andrena hippotes", "Andrena ang…
## $ verbatimElevation <dbl> 71, 71, 71, 62, 1271, 1271, 1271, 1271, 1271, 37, 37…
## $ decimalLatitude <dbl> 44.5599, 44.5599, 44.5599, 45.6684, 42.4885, 42.4885…
## $ decimalLongitude <dbl> -123.2883, -123.2883, -123.2883, -121.2457, -121.876…Load raster data
Cover2024_rast <- rast("~/Documents/Bees-R-Cool-SJ/Data-sets/NLCD/land-cover/2024_NLCD_LndCov_CU_C1V1_mi67k2ad1apaqe.tiff")
summary(values(Cover2024_rast))## 2024_NLCD_LndCov_CU_C1V1_mi67k2ad1apaqe
## Min. :11
## 1st Qu.:42
## Median :42
## Mean :50
## 3rd Qu.:52
## Max. :95
## NA's :120709810crs(Cover2024_rast, proj = TRUE)## [1] "+proj=aea +lat_0=23 +lon_0=-96 +lat_1=29.5 +lat_2=45.5 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs"nlcd_ID <- tribble(
~landcover, ~NLCD_name,
11, "Open Water",
12, "Perennial Ice/Snow",
21, "Developed, Open Space",
22, "Developed, Low Intensity",
23, "Developed, Medium Intensity",
24, "Developed, High Intensity",
31, "Barren Land",
41, "Deciduous Forest",
42, "Evergreen Forest",
43, "Mixed Forest",
51, "Dwarf Scrub",
52, "Shrub/Scrub",
71, "Grassland/Herbaceous",
72, "Sedge/Herbaceous",
73, "Lichens",
74, "Moss",
81, "Pasture/Hay",
82, "Cultivated Crops",
90, "Woody Wetlands",
95, "Emergent Herbaceous Wetlands"
)Load Ecoregion data
eco <- st_read("~/Documents/Bees-R-Cool-SJ/Data-sets/or_eco_l3/or_eco_l3.shp")## Reading layer `or_eco_l3' from data source
## `/Users/shanielynn/Documents/Bees-R-Cool-SJ/Data-sets/or_eco_l3/or_eco_l3.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 9 features and 13 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -2294708 ymin: 2301515 xmax: -1584307 ymax: 2900120
## Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic_USGS_versioncrs(eco, proj = TRUE)## [1] "+proj=aea +lat_0=23 +lon_0=-96 +lat_1=29.5 +lat_2=45.5 +x_0=0 +y_0=0 +datum=NAD83 +units=m +no_defs"colnames(eco)## [1] "US_L3CODE" "US_L3NAME" "NA_L3CODE" "NA_L3NAME" "NA_L2CODE"
## [6] "NA_L2NAME" "NA_L1CODE" "NA_L1NAME" "STATE_NAME" "EPA_REGION"
## [11] "L3_KEY" "L2_KEY" "L1_KEY" "geometry"# Convert oba to sf
oba_sf <- st_as_sf(oba, coords = c("decimalLongitude", "decimalLatitude"), crs = 4326)
oba_sf <- st_transform(oba_sf, crs(Cover2024_rast))# Splitting has to be done first due to large data file otherwise it wont load on my computer
cascade <- eco %>%
filter(US_L3NAME %in% c("Cascades"))
coast <- eco %>%
filter(US_L3NAME %in% c("Coast Range"))
print(cascade)## Simple feature collection with 1 feature and 13 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -2165913 ymin: 2419393 xmax: -1968927 ymax: 2795738
## Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic_USGS_version
## US_L3CODE US_L3NAME NA_L3CODE NA_L3NAME NA_L2CODE NA_L2NAME
## 1 4 Cascades 6.2.7 Cascades 6.2 WESTERN CORDILLERA
## NA_L1CODE NA_L1NAME STATE_NAME EPA_REGION L3_KEY
## 1 6 NORTHWESTERN FORESTED MOUNTAINS Oregon 10 4 Cascades
## L2_KEY L1_KEY
## 1 6.2 WESTERN CORDILLERA 6 NORTHWESTERN FORESTED MOUNTAINS
## geometry
## 1 MULTIPOLYGON (((-2000421 25...print(coast)## Simple feature collection with 1 feature and 13 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -2294708 ymin: 2447788 xmax: -2055067 ymax: 2900120
## Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic_USGS_version
## US_L3CODE US_L3NAME NA_L3CODE NA_L3NAME NA_L2CODE
## 1 1 Coast Range 7.1.8 Coast Range 7.1
## NA_L2NAME NA_L1CODE NA_L1NAME STATE_NAME
## 1 MARINE WEST COAST FOREST 7 MARINE WEST COAST FOREST Oregon
## EPA_REGION L3_KEY L2_KEY
## 1 10 1 Coast Range 7.1 MARINE WEST COAST FOREST
## L1_KEY geometry
## 1 7 MARINE WEST COAST FOREST MULTIPOLYGON (((-2110046 28...crs(cascade, proj = TRUE)## [1] "+proj=aea +lat_0=23 +lon_0=-96 +lat_1=29.5 +lat_2=45.5 +x_0=0 +y_0=0 +datum=NAD83 +units=m +no_defs"crs(coast, proj = TRUE)## [1] "+proj=aea +lat_0=23 +lon_0=-96 +lat_1=29.5 +lat_2=45.5 +x_0=0 +y_0=0 +datum=NAD83 +units=m +no_defs"unique(cascade$US_L3NAME) # CHECK WHAT THIS DOES## [1] "Cascades"unique(coast$US_L3NAME)## [1] "Coast Range"cascade_crs <- st_transform(cascade, crs(Cover2024_rast))
coast_crs <- st_transform(coast, crs(Cover2024_rast))
cascade_rast <- mask(crop(Cover2024_rast, vect(cascade_crs)), vect(cascade_crs))## |---------|---------|---------|---------|========================================= |---------|---------|---------|---------|========================================= |---------|---------|---------|---------|========================================= coast_rast <- mask(crop(Cover2024_rast, vect(coast_crs)), vect(coast_crs))## |---------|---------|---------|---------|========================================= |---------|---------|---------|---------|========================================= |---------|---------|---------|---------|========================================= crs(cascade_crs, proj = TRUE)## [1] "+proj=aea +lat_0=23 +lon_0=-96 +lat_1=29.5 +lat_2=45.5 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs"crs(Cover2024_rast, proj = TRUE)## [1] "+proj=aea +lat_0=23 +lon_0=-96 +lat_1=29.5 +lat_2=45.5 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs"turn regions into data.frames
cascade_df <- as.data.frame(cascade_rast, xy = TRUE, na.rm = TRUE)
coast_df <- as.data.frame(coast_rast, xy = TRUE, na.rm = TRUE)
cascade_df <- cascade_df %>%
rename(LndCov = `2024_NLCD_LndCov_CU_C1V1_mi67k2ad1apaqe`)
coast_df <- coast_df %>%
rename(LndCov = `2024_NLCD_LndCov_CU_C1V1_mi67k2ad1apaqe`)
colnames(cascade_df)## [1] "x" "y" "LndCov"colnames(coast_df)## [1] "x" "y" "LndCov"# filter bee points for each ecoregion
oba_cascade <- oba_sf[cascade_crs, ]
oba_coast <- oba_sf[coast_crs, ]
names(oba_cascade)## [1] "day" "month" "year"
## [4] "genus" "GenusSpecies" "verbatimElevation"
## [7] "geometry"names(cascade)## [1] "US_L3CODE" "US_L3NAME" "NA_L3CODE" "NA_L3NAME" "NA_L2CODE"
## [6] "NA_L2NAME" "NA_L1CODE" "NA_L1NAME" "STATE_NAME" "EPA_REGION"
## [11] "L3_KEY" "L2_KEY" "L1_KEY" "geometry"# Double check to make sure code worked as it should
oba_cascade$GenusSpecies %in%
oba_coast$GenusSpecies## [1] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
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## [2521] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [2533] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2545] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
## [2557] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2569] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2581] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2593] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2605] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
## [2617] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2629] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2641] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2653] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2665] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2677] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2689] TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
## [2701] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2713] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2725] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2737] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2749] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2761] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2773] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
## [2785] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2797] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
## [2809] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2821] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
## [2833] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
## [2845] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE
## [2857] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2869] TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE FALSE
## [2881] TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
## [2893] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2905] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2917] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2929] FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE
## [2941] FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE FALSE FALSE
## [2953] FALSE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [2965] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2977] TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
## [2989] TRUE FALSE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
## [3001] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3013] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
## [3025] TRUE TRUE TRUE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE
## [3037] FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE TRUE FALSE
## [3049] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE
## [3061] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3073] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [3085] FALSE TRUE FALSE FALSE TRUE FALSE TRUE FALSE TRUE TRUE TRUE TRUE
## [3097] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
## [3109] FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [3121] TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
## [3133] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
## [3145] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE
## [3157] FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE
## [3169] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3181] TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
## [3193] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [3205] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3217] TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
## [3229] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3241] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3253] TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
## [3265] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
## [3277] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3289] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3301] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3313] TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE
## [3325] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3337] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3349] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3361] TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
## [3373] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3385] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
## [3397] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3409] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
## [3421] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE FALSE
## [3433] TRUE TRUE TRUE FALSE FALSE TRUE FALSE TRUE TRUE TRUE FALSE TRUE
## [3445] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3457] TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
## [3469] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3481] TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE FALSE TRUE TRUE TRUE
## [3493] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE
## [3505] FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3517] TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
## [3529] TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE FALSE FALSE TRUE
## [3541] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3553] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3565] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3589] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE
## [3601] TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
## [3613] FALSE TRUE TRUE TRUE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE
## [3625] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [3637] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE
## [3649] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
## [3661] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3673] FALSE TRUE FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
## [3685] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3697] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3709] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3721] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3733] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3745] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3757] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3769] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3781] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3793] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3805] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3817] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3829] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3841] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3853] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3865] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3877] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3889] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3901] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3913] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3925] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3937] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3949] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3961] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3973] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3985] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3997] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4009] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
## [4021] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4033] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4045] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4057] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE TRUE TRUE
## [4069] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4081] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
## [4093] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE
## [4105] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4117] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4129] TRUE TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE
## [4141] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4153] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
## [4165] FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE FALSE FALSE
## [4177] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE
## [4189] FALSE TRUE FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
## [4201] TRUE FALSE TRUE FALSE FALSE TRUE FALSE TRUE TRUE TRUE FALSE TRUE
## [4213] TRUE TRUE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE
## [4225] TRUE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4237] TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4249] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4261] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
## [4273] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4285] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [4297] FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
## [4309] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4321] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4333] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4345] TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
## [4357] TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE
## [4369] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
## [4381] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
## [4393] TRUE TRUE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
## [4405] TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
## [4417] FALSE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4429] TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4441] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4453] FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [4465] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4477] TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE
## [4489] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4501] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4513] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
## [4525] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE
## [4537] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
## [4549] FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE
## [4561] FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE
## [4573] TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4585] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
## [4597] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
## [4609] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4621] TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE
## [4633] TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
## [4645] TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
## [4657] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4669] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE
## [4681] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4693] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4705] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [4717] FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [4729] FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
## [4741] FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
## [4753] FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
## [4765] TRUE TRUE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
## [4777] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4789] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4801] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4813] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [4825] FALSE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4837] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4849] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4861] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4873] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4885] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4897] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4909] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4921] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4933] TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4945] TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE
## [4957] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
## [4969] TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE FALSE TRUE
## [4981] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4993] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5005] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5017] FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5029] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
## [5041] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5053] TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
## [5065] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5077] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5089] TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
## [5101] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5113] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5125] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5137] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5149] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5161] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5173] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5185] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [5197] TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE TRUE
## [5209] TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5221] TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5233] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5245] TRUE TRUE FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
## [5257] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5269] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5281] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
## [5293] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5305] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
## [5317] TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE FALSE TRUE TRUE TRUE
## [5329] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
## [5341] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5353] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5365] TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [5377] FALSE TRUE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5389] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
## [5401] TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
## [5413] TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
## [5425] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE
## [5437] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5449] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5461] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5473] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5485] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5497] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5509] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5521] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5533] FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE
## [5545] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5557] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
## [5569] TRUE TRUE FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE
## [5581] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE
## [5593] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5605] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
## [5617] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5629] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5641] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5653] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5665] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5677] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5689] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5701] TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE
## [5713] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5725] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5737] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5749] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
## [5761] TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE
## [5773] TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE
## [5785] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5797] TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [5809] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5821] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5833] FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
## [5845] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5857] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5869] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5881] TRUE FALSE FALSE TRUE TRUE FALSE TRUE TRUE FALSE FALSE TRUE TRUE
## [5893] TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5905] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5917] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5929] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5941] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5953] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
## [5965] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE
## [5977] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5989] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6001] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
## [6013] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
## [6025] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6037] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6049] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6061] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
## [6073] TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE
## [6085] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6097] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [6109] TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE
## [6121] FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE
## [6133] TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
## [6145] FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
## [6157] FALSE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6169] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [6181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6193] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
## [6205] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6217] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6229] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE
## [6241] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6253] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6265] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [6277] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6289] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6301] FALSE TRUE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6313] TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
## [6325] TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE
## [6337] TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6349] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6361] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
## [6373] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6385] TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE
## [6397] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6409] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [6421] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6433] FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE TRUE
## [6445] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6457] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6469] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6481] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6493] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [6505] FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
## [6517] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6529] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE
## [6541] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6553] TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE
## [6565] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE
## [6577] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
## [6589] TRUE FALSE FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE FALSE TRUE
## [6601] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6613] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6625] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6637] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6649] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6661] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
## [6673] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE
## [6685] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
## [6697] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6709] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
## [6721] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
## [6733] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
## [6745] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
## [6757] FALSE TRUE FALSE FALSE TRUE TRUE FALSE TRUE TRUE FALSE TRUE TRUE
## [6769] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
## [6781] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
## [6793] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6805] TRUE FALSE TRUE TRUE TRUE TRUEVisualization Barplots and Maps
top_cascade <- oba_cascade %>%
st_drop_geometry() %>%
count(GenusSpecies, sort = TRUE) %>%
slice_head(n = 10)
top_casc_sf <- oba_cascade %>%
filter(GenusSpecies %in% top_cascade$GenusSpecies)
# Bar plot of the top cascade bees (GenusSpecies)
ggplot(top_cascade, aes(x = reorder(GenusSpecies, n), y = n, fill = GenusSpecies)) +
geom_col() +
scale_fill_brewer(palette = "BrBG") +
coord_flip() +
labs(title = "Cascades: Most Abundant",
x = "Species",
y = "Count") +
theme_minimal(base_size = 14) +
theme(panel.background = element_rect(fill = "lightgray"),
legend.position = "none",
axis.title.y = element_blank())
plot cascade w/ no raster
ggplot() +
geom_sf(data = cascade_crs, fill = "gray", color = "black", linewidth = 0.3) +
geom_sf(data = top_casc_sf, aes(color = GenusSpecies), size = 0.5) + # adjust the size?
scale_color_brewer(palette = "Spectral") +
coord_sf() +
theme_minimal(base_size = 10) +
theme(legend.key.size = unit(0.3, "lines"),
legend.text = element_text(size = 6),
legend.box = "vertical",
legend.position = "right",
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) + # 45 degree angle
guides(color = guide_legend(ncol = 1, keyheight = unit(0.3, "lines"))) +
ggtitle("Top 10 Cascade Species") +
labs(x = "Latitude", y = "Longitude", color = "Species")
# Bar plot of the top coast bees (GenusSpecies)
top_coast <- oba_coast %>%
st_drop_geometry() %>%
count(GenusSpecies, sort = TRUE) %>%
slice_head(n = 10)
top_coast_sf <- oba_coast %>%
filter(GenusSpecies %in% top_coast$GenusSpecies)
# Bar plot of the top coast bees (GenusSpecies)
ggplot(top_coast, aes(x = reorder(GenusSpecies, n), y = n, fill = GenusSpecies)) +
geom_col() +
scale_fill_brewer(palette = "BrBG") +
coord_flip() +
labs(title = "Coast: Most Abundant",
x = "Species",
y = "Count") +
theme_minimal(base_size = 14) +
theme(panel.background = element_rect(fill = "lightgray"),
legend.position = "none",
axis.title.y = element_blank())
plot coast w/ no raster
ggplot() +
geom_sf(data = coast_crs, fill = "gray", color = "black", linewidth = 0.3) +
geom_sf(data = top_coast_sf, aes(color = GenusSpecies), size = 0.5) + # adjust the size?
scale_color_brewer(palette = "Spectral") +
coord_sf() +
theme_minimal(base_size = 10) +
theme(legend.key.size = unit(0.3, "lines"),
legend.text = element_text(size = 6),
legend.box = "vertical",
legend.position = "right",
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) + # 45 degree angle
guides(color = guide_legend(ncol = 1, keyheight = unit(0.3, "lines"))) +
ggtitle("Top 10 Cascade Species") +
labs(x = "Latitude", y = "Longitude", color = "Species")
Associate your bee data points with the NLCD raster data: for each bee point, determine what landcover type it falls on.
# Extract NLCD land cover for each bee point
top_casc_sf$LndCov <- terra::extract(cascade_rast, vect(top_casc_sf))[,2]
top_coast_sf$LndCov <- terra::extract(coast_rast, vect(top_coast_sf))[,2]Join NLCD legend names
top_casc_sf <- top_casc_sf %>%
left_join(nlcd_ID, by = c("LndCov" = "landcover"))
top_coast_sf <- top_coast_sf %>%
left_join(nlcd_ID, by = c("LndCov" = "landcover"))Summarize landcover data for each region
# Cascade
cascade_land_sum <- top_casc_sf %>%
st_drop_geometry() %>%
group_by(NLCD_name) %>%
summarise(count = n()) %>%
arrange(desc(count))
# Coast
coast_land_sum <- top_coast_sf %>%
st_drop_geometry() %>%
group_by(NLCD_name) %>%
summarise(count = n()) %>%
arrange(desc(count))
cascade_land_sum## # A tibble: 14 × 2
## NLCD_name count
## <chr> <int>
## 1 Evergreen Forest 1426
## 2 Shrub/Scrub 778
## 3 Developed, Open Space 555
## 4 Developed, Low Intensity 250
## 5 Grassland/Herbaceous 92
## 6 Woody Wetlands 85
## 7 Developed, Medium Intensity 71
## 8 Open Water 25
## 9 Pasture/Hay 21
## 10 Emergent Herbaceous Wetlands 20
## 11 Barren Land 17
## 12 Mixed Forest 16
## 13 Developed, High Intensity 13
## 14 Deciduous Forest 1coast_land_sum## # A tibble: 14 × 2
## NLCD_name count
## <chr> <int>
## 1 Evergreen Forest 879
## 2 Developed, Low Intensity 417
## 3 Developed, Open Space 363
## 4 Pasture/Hay 352
## 5 Emergent Herbaceous Wetlands 256
## 6 Shrub/Scrub 198
## 7 Barren Land 160
## 8 Woody Wetlands 149
## 9 Mixed Forest 139
## 10 Developed, Medium Intensity 134
## 11 Grassland/Herbaceous 41
## 12 Deciduous Forest 31
## 13 Open Water 26
## 14 Developed, High Intensity 19Summarize land cover data for each region and species
# Cascade: counts of each bee species per land cover type
cascade_species_sum <- top_casc_sf %>%
st_drop_geometry() %>% # remove spatial info for easy summarizing
group_by(NLCD_name, GenusSpecies) %>%
summarise(count = n(), .groups = "drop")
# Coast: same for coast region
coast_species_sum <- top_coast_sf %>%
st_drop_geometry() %>%
group_by(NLCD_name, GenusSpecies) %>%
summarise(count = n(), .groups = "drop")
cascade_species_sum## # A tibble: 95 × 3
## NLCD_name GenusSpecies count
## <chr> <chr> <int>
## 1 Barren Land Anthophora urbana 2
## 2 Barren Land Bombus mixtus 3
## 3 Barren Land Bombus vancouverensis 7
## 4 Barren Land Bombus vosnesenskii 3
## 5 Barren Land Ceratina acantha 1
## 6 Barren Land Halictus farinosus 1
## 7 Deciduous Forest Bombus flavifrons 1
## 8 Developed, High Intensity Bombus flavifrons 1
## 9 Developed, High Intensity Bombus vancouverensis 5
## 10 Developed, High Intensity Bombus vosnesenskii 1
## # ℹ 85 more rowscoast_species_sum## # A tibble: 121 × 3
## NLCD_name GenusSpecies count
## <chr> <chr> <int>
## 1 Barren Land Bombus caliginosus 30
## 2 Barren Land Bombus flavifrons 1
## 3 Barren Land Bombus mixtus 44
## 4 Barren Land Bombus sitkensis 3
## 5 Barren Land Bombus vosnesenskii 46
## 6 Barren Land Ceratina acantha 1
## 7 Barren Land Halictus ligatus 2
## 8 Barren Land Halictus rubicundus 13
## 9 Barren Land Lasioglossum villosulum 20
## 10 Deciduous Forest Bombus caliginosus 3
## # ℹ 111 more rows# Top abundant species by land cover (Cascades)
ggplot(cascade_species_sum, aes(x = NLCD_name, y = count, fill = GenusSpecies)) +
geom_bar(stat = "identity", position = "stack") +
scale_fill_brewer(palette = "BrBG") +
theme_minimal() +
labs(title = "Top Abundant Species by Land Cover (Cascades)",
y = "Count", fill = "Species") +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
panel.background = element_rect(fill = "lightgray"),
legend.key.size = unit(0.3, "lines"),
axis.title.x = element_blank())
ggplot(coast_species_sum, aes(x = NLCD_name, y = count, fill = GenusSpecies)) +
geom_bar(stat = "identity", position = "stack") +
scale_fill_brewer(palette = "BrBG") +
theme_minimal() +
labs(title = "Top Abundant Species by Land Cover (Coast)",
x = "Land Cover Type",
y = "Count", fill = "Species") +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
panel.background = element_rect(fill = "lightgray"),
legend.key.size = unit(0.3, "lines"),
axis.title.x = element_blank())
Analysis/ Hypothesis Testing
Statistical Analysis Section
# key function: assign as tvd
tvd <- function(dist1, dist2) { # Total variation distance
sum(abs(dist1 - dist2)) / 2
}
# Compute observed TVD
cascade_n <- oba_cascade %>%
st_drop_geometry() %>%
count(GenusSpecies, name = "cascade_count")
coast_n <- oba_coast %>%
st_drop_geometry() %>%
count(GenusSpecies, name = "coast_count")
species_df <- full_join(cascade_n, coast_n, by = "GenusSpecies") %>%
replace_na(list(cascade_count = 0, coast_count = 0))
bee_tvd <- tvd(species_df$cascade_count, species_df$coast_count)
bee_tvd## [1] 3311.5A/B testing
## proofread and run again before submitting
set.seed(123) # for reproducibility
# Combine both regions into a single dataset
combined <- rbind(
oba_cascade %>% st_drop_geometry() %>% mutate(region = "cascade"),
oba_coast %>% st_drop_geometry() %>% mutate(region = "coast")
)
# Number of bees in each region (kept fixed during shuffling)
n_cascade <- sum(combined$region == "cascade")
n_coast <- sum(combined$region == "coast")
# Number of permutations
n_perm <- 1000
# Storage vector for permuted TVD values
perm_tvd <- numeric(n_perm)
for (i in 1:n_perm) {
# Shuffle region labels
shuffled <- combined %>%
mutate(region = sample(region)) # randomly permutes cascade/coast labels
# Recalculate species counts for each shuffled region
cascade_shuffled <- shuffled %>%
filter(region == "cascade") %>%
count(GenusSpecies, name = "cascade_count")
coast_shuffled <- shuffled %>%
filter(region == "coast") %>%
count(GenusSpecies, name = "coast_count")
# Join so that missing species get zeros
shuffled_join <- full_join(cascade_shuffled, coast_shuffled, by = "GenusSpecies") %>%
replace_na(list(cascade_count = 0, coast_count = 0))
# Compute permuted TVD
perm_tvd[i] <- tvd(shuffled_join$cascade_count, shuffled_join$coast_count)
}
# Show permutation-based p-value
p_value <- (sum(perm_tvd >= bee_tvd) + 1) / (n_perm + 1)
p_value## [1] 0.000999001plot(mtcars$mpg, mtcars$wt)
Look into doing the following, someone else was able to run it with theirs. See waht parameters I need
(Optional) Shannon Index
# Extract NLCD raster values to bee points
oba_cascade$LndCov <- terra::extract(cascade_rast, oba_cascade)$`2024_NLCD_LndCov_CU_C1V1_mi67k2ad1apaqe`
oba_coast$LndCov <- terra::extract(coast_rast, oba_coast)$`2024_NLCD_LndCov_CU_C1V1_mi67k2ad1apaqe`
# Join landcover codes to NLCD names
oba_cascade <- oba_cascade %>%
left_join(nlcd_ID, by = c("LndCov" = "landcover"))
oba_coast <- oba_coast %>%
left_join(nlcd_ID, by = c("LndCov" = "landcover"))
# Create a species x site matrix (site = NLCD type)
cascade_matrix <- table(oba_cascade$NLCD_name, oba_cascade$GenusSpecies)
# Shannon diversity per land cover
diversity(cascade_matrix, index = "shannon")## Barren Land Deciduous Forest
## 2.789241 0.000000
## Developed, High Intensity Developed, Low Intensity
## 1.454867 3.810631
## Developed, Medium Intensity Developed, Open Space
## 2.927092 3.579398
## Emergent Herbaceous Wetlands Evergreen Forest
## 3.202978 4.103190
## Grassland/Herbaceous Mixed Forest
## 3.702190 2.532410
## Open Water Pasture/Hay
## 1.561824 1.352292
## Shrub/Scrub Woody Wetlands
## 3.770405 2.661553# Species richness
specnumber(cascade_matrix)## Barren Land Deciduous Forest
## 20 1
## Developed, High Intensity Developed, Low Intensity
## 6 96
## Developed, Medium Intensity Developed, Open Space
## 36 114
## Emergent Herbaceous Wetlands Evergreen Forest
## 31 198
## Grassland/Herbaceous Mixed Forest
## 73 17
## Open Water Pasture/Hay
## 9 9
## Shrub/Scrub Woody Wetlands
## 153 27